





'''simple docstring'''

import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    "split_dict"		,			[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"		,			num_bytes=13_37		,			num_examples=42		,			dataset_name="my_dataset"			)}			),
        SplitDict({"train": SplitInfo(name="train"		,			num_bytes=13_37		,			num_examples=42			)}			),
        SplitDict({"train": SplitInfo()}			),
    ]		,			)
def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     split_dict._to_yaml_list()
    assert len(_lowerCamelCase			) == len(_lowerCamelCase			)
    A__							=     SplitDict._from_yaml_list(_lowerCamelCase			)
    for split_name, split_info in split_dict.items():
        # dataset_name field is deprecated, and is therefore not part of the YAML dump
        A__							=     None
        # the split name of split_dict takes over the name of the split info object
        A__							=     split_name
    assert split_dict == reloaded





@pytest.mark.parametrize(
    "split_info"		,			[SplitInfo(), SplitInfo(dataset_name=_lowerCamelCase			), SplitInfo(dataset_name="my_dataset"			)]			)
def 		UpperCamelCase							(				_lowerCamelCase   :   Any			):
    # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
    # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
    A__							=     asdict(SplitDict({"train": split_info}			)			)
    assert "dataset_name" in split_dict_asdict["train"]
    assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
